Overview

Brought to you by YData

Dataset statistics

Number of variables24
Number of observations483
Missing cells1874
Missing cells (%)16.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory306.8 KiB
Average record size in memory650.5 B

Variable types

Numeric6
Text8
Categorical10

Alerts

Chiffre is highly overall correlated with Reglements and 1 other fieldsHigh correlation
Etat is highly overall correlated with FournitPF and 5 other fieldsHigh correlation
FournitPF is highly overall correlated with Etat and 6 other fieldsHigh correlation
IDCGAFournisseur is highly overall correlated with IDPaysHigh correlation
IDCategorie is highly overall correlated with IDPays and 3 other fieldsHigh correlation
IDDevise is highly overall correlated with Etat and 4 other fieldsHigh correlation
IDFournisseur is highly overall correlated with Etat and 5 other fieldsHigh correlation
IDModeReglement is highly overall correlated with Etat and 4 other fieldsHigh correlation
IDPays is highly overall correlated with Etat and 5 other fieldsHigh correlation
IsMP is highly overall correlated with PaysHigh correlation
IsPF is highly overall correlated with IDFournisseurHigh correlation
Observations is highly overall correlated with PaysHigh correlation
Pays is highly overall correlated with FournitPF and 5 other fieldsHigh correlation
Reglements is highly overall correlated with Chiffre and 2 other fieldsHigh correlation
Solde is highly overall correlated with ChiffreHigh correlation
isService is highly overall correlated with Etat and 6 other fieldsHigh correlation
Observations is highly imbalanced (97.3%) Imbalance
IDModeReglement is highly imbalanced (68.2%) Imbalance
IDCGAFournisseur is highly imbalanced (81.2%) Imbalance
IsMP is highly imbalanced (97.9%) Imbalance
Adresse has 37 (7.7%) missing values Missing
Email has 285 (59.0%) missing values Missing
Tel has 213 (44.1%) missing values Missing
Fax has 420 (87.0%) missing values Missing
MF has 272 (56.3%) missing values Missing
Pays has 419 (86.7%) missing values Missing
CodeTVA has 228 (47.2%) missing values Missing
IDFournisseur has unique values Unique
Code has unique values Unique
Chiffre has 126 (26.1%) zeros Zeros
Reglements has 189 (39.1%) zeros Zeros
Solde has 260 (53.8%) zeros Zeros
IDCategorie has 72 (14.9%) zeros Zeros
IDPays has 131 (27.1%) zeros Zeros

Reproduction

Analysis started2025-03-19 16:50:35.327111
Analysis finished2025-03-19 16:50:41.409298
Duration6.08 seconds
Software versionydata-profiling vv4.13.0
Download configurationconfig.json

Variables

IDFournisseur
Real number (ℝ)

High correlation  Unique 

Distinct483
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean440.61905
Minimum6
Maximum713
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2025-03-19T17:50:41.470071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile105.2
Q1327.5
median455
Q3583.5
95-th percentile684.9
Maximum713
Range707
Interquartile range (IQR)256

Descriptive statistics

Standard deviation174.33757
Coefficient of variation (CV)0.39566508
Kurtosis-0.54188333
Mean440.61905
Median Absolute Deviation (MAD)128
Skewness-0.47515371
Sum212819
Variance30393.589
MonotonicityStrictly increasing
2025-03-19T17:50:41.569671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
713 1
 
0.2%
6 1
 
0.2%
19 1
 
0.2%
696 1
 
0.2%
692 1
 
0.2%
691 1
 
0.2%
690 1
 
0.2%
689 1
 
0.2%
688 1
 
0.2%
687 1
 
0.2%
Other values (473) 473
97.9%
ValueCountFrequency (%)
6 1
0.2%
19 1
0.2%
25 1
0.2%
29 1
0.2%
32 1
0.2%
33 1
0.2%
38 1
0.2%
39 1
0.2%
42 1
0.2%
46 1
0.2%
ValueCountFrequency (%)
713 1
0.2%
712 1
0.2%
711 1
0.2%
710 1
0.2%
709 1
0.2%
708 1
0.2%
707 1
0.2%
706 1
0.2%
705 1
0.2%
704 1
0.2%

Fournisseur
Text

Distinct481
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size35.7 KiB
2025-03-19T17:50:41.769773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length50
Median length35
Mean length18.63354
Min length2

Characters and Unicode

Total characters9000
Distinct characters66
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique479 ?
Unique (%)99.2%

Sample

1st rowBALINLER PAZARLAMA VE TICARET
2nd rowDRISHTI APPARELS
3rd rowTRUMODE INTERNATIONAL LTD.
4th rowSHANGHAI SILK GROUP TRADING DEVELOPMENT CO., LTD
5th rowGULEKS TEKSTIL SAN TIC LTD STI
ValueCountFrequency (%)
naf 53
 
3.7%
ltd 27
 
1.9%
sas 23
 
1.6%
de 20
 
1.4%
sci 17
 
1.2%
co 16
 
1.1%
france 14
 
1.0%
cc 13
 
0.9%
san 13
 
0.9%
tekstil 13
 
0.9%
Other values (900) 1237
85.5%
2025-03-19T17:50:42.066580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
965
 
10.7%
A 900
 
10.0%
E 836
 
9.3%
S 638
 
7.1%
N 624
 
6.9%
I 617
 
6.9%
T 509
 
5.7%
R 463
 
5.1%
L 407
 
4.5%
O 403
 
4.5%
Other values (56) 2638
29.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 7667
85.2%
Space Separator 965
 
10.7%
Decimal Number 131
 
1.5%
Other Punctuation 102
 
1.1%
Dash Punctuation 60
 
0.7%
Lowercase Letter 60
 
0.7%
Close Punctuation 7
 
0.1%
Open Punctuation 7
 
0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 900
11.7%
E 836
10.9%
S 638
 
8.3%
N 624
 
8.1%
I 617
 
8.0%
T 509
 
6.6%
R 463
 
6.0%
L 407
 
5.3%
O 403
 
5.3%
C 376
 
4.9%
Other values (16) 1894
24.7%
Lowercase Letter
ValueCountFrequency (%)
a 10
16.7%
o 7
11.7%
n 6
10.0%
u 5
8.3%
t 4
 
6.7%
i 4
 
6.7%
s 3
 
5.0%
r 3
 
5.0%
e 3
 
5.0%
l 3
 
5.0%
Other values (9) 12
20.0%
Decimal Number
ValueCountFrequency (%)
2 20
15.3%
5 16
12.2%
3 16
12.2%
8 15
11.5%
4 13
9.9%
6 13
9.9%
1 12
9.2%
9 11
8.4%
7 10
7.6%
0 5
 
3.8%
Other Punctuation
ValueCountFrequency (%)
. 62
60.8%
' 25
24.5%
& 6
 
5.9%
, 5
 
4.9%
/ 3
 
2.9%
: 1
 
1.0%
Space Separator
ValueCountFrequency (%)
965
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 60
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7727
85.9%
Common 1273
 
14.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 900
11.6%
E 836
10.8%
S 638
 
8.3%
N 624
 
8.1%
I 617
 
8.0%
T 509
 
6.6%
R 463
 
6.0%
L 407
 
5.3%
O 403
 
5.2%
C 376
 
4.9%
Other values (35) 1954
25.3%
Common
ValueCountFrequency (%)
965
75.8%
. 62
 
4.9%
- 60
 
4.7%
' 25
 
2.0%
2 20
 
1.6%
5 16
 
1.3%
3 16
 
1.3%
8 15
 
1.2%
4 13
 
1.0%
6 13
 
1.0%
Other values (11) 68
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
965
 
10.7%
A 900
 
10.0%
E 836
 
9.3%
S 638
 
7.1%
N 624
 
6.9%
I 617
 
6.9%
T 509
 
5.7%
R 463
 
5.1%
L 407
 
4.5%
O 403
 
4.5%
Other values (56) 2638
29.3%

Adresse
Text

Missing 

Distinct403
Distinct (%)90.4%
Missing37
Missing (%)7.7%
Memory size46.4 KiB
2025-03-19T17:50:42.263904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length149
Median length69
Mean length38.091928
Min length1

Characters and Unicode

Total characters16989
Distinct characters92
Distinct categories13 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique388 ?
Unique (%)87.0%

Sample

1st rowGULTEPE MAH TURE SOK N°12
2nd rowPLOT NO 180 SECTOR -6 IM MANES
3rd row3-5F N0 9. LOFT POWER N0 28 XI
4th rowN°283 WUXING ROAD
5th rowAMBARLI PETROL OFSI DOLUM
ValueCountFrequency (%)
rue 155
 
5.3%
de 93
 
3.2%
paris 90
 
3.1%
83
 
2.8%
du 49
 
1.7%
la 47
 
1.6%
avenue 44
 
1.5%
des 41
 
1.4%
cedex 38
 
1.3%
cs 30
 
1.0%
Other values (1383) 2262
77.1%
2025-03-19T17:50:42.579834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2405
 
14.2%
E 1039
 
6.1%
A 828
 
4.9%
R 687
 
4.0%
0 652
 
3.8%
I 570
 
3.4%
N 540
 
3.2%
e 522
 
3.1%
S 510
 
3.0%
L 497
 
2.9%
Other values (82) 8739
51.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 8043
47.3%
Lowercase Letter 3084
 
18.2%
Decimal Number 2722
 
16.0%
Space Separator 2405
 
14.2%
Other Punctuation 346
 
2.0%
Control 191
 
1.1%
Dash Punctuation 172
 
1.0%
Other Symbol 13
 
0.1%
Close Punctuation 4
 
< 0.1%
Open Punctuation 4
 
< 0.1%
Other values (3) 5
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 522
16.9%
a 291
9.4%
r 248
 
8.0%
n 244
 
7.9%
u 243
 
7.9%
i 223
 
7.2%
o 180
 
5.8%
s 178
 
5.8%
d 163
 
5.3%
l 155
 
5.0%
Other values (22) 637
20.7%
Uppercase Letter
ValueCountFrequency (%)
E 1039
12.9%
A 828
 
10.3%
R 687
 
8.5%
I 570
 
7.1%
N 540
 
6.7%
S 510
 
6.3%
L 497
 
6.2%
U 440
 
5.5%
C 376
 
4.7%
O 368
 
4.6%
Other values (21) 2188
27.2%
Decimal Number
ValueCountFrequency (%)
0 652
24.0%
1 374
13.7%
2 301
11.1%
7 266
9.8%
5 258
 
9.5%
3 219
 
8.0%
9 180
 
6.6%
6 180
 
6.6%
4 174
 
6.4%
8 118
 
4.3%
Other Punctuation
ValueCountFrequency (%)
, 218
63.0%
. 53
 
15.3%
' 33
 
9.5%
/ 26
 
7.5%
: 11
 
3.2%
" 2
 
0.6%
? 2
 
0.6%
# 1
 
0.3%
Dash Punctuation
ValueCountFrequency (%)
- 169
98.3%
– 3
 
1.7%
Control
ValueCountFrequency (%)
96
50.3%
95
49.7%
Space Separator
ValueCountFrequency (%)
2405
100.0%
Other Symbol
ValueCountFrequency (%)
° 13
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 2
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11127
65.5%
Common 5862
34.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 1039
 
9.3%
A 828
 
7.4%
R 687
 
6.2%
I 570
 
5.1%
N 540
 
4.9%
e 522
 
4.7%
S 510
 
4.6%
L 497
 
4.5%
U 440
 
4.0%
C 376
 
3.4%
Other values (53) 5118
46.0%
Common
ValueCountFrequency (%)
2405
41.0%
0 652
 
11.1%
1 374
 
6.4%
2 301
 
5.1%
7 266
 
4.5%
5 258
 
4.4%
3 219
 
3.7%
, 218
 
3.7%
9 180
 
3.1%
6 180
 
3.1%
Other values (19) 809
 
13.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16913
99.6%
None 71
 
0.4%
Punctuation 5
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2405
 
14.2%
E 1039
 
6.1%
A 828
 
4.9%
R 687
 
4.1%
0 652
 
3.9%
I 570
 
3.4%
N 540
 
3.2%
e 522
 
3.1%
S 510
 
3.0%
L 497
 
2.9%
Other values (67) 8663
51.2%
None
ValueCountFrequency (%)
é 32
45.1%
° 13
18.3%
â 4
 
5.6%
ö 3
 
4.2%
è 3
 
4.2%
ç 3
 
4.2%
ü 3
 
4.2%
Ü 3
 
4.2%
 3
 
4.2%
Ö 1
 
1.4%
Other values (3) 3
 
4.2%
Punctuation
ValueCountFrequency (%)
– 3
60.0%
’ 2
40.0%

Code
Text

Unique 

Distinct483
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size30.3 KiB
2025-03-19T17:50:42.736085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length25
Median length8
Mean length7.3374741
Min length1

Characters and Unicode

Total characters3544
Distinct characters43
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique483 ?
Unique (%)100.0%

Sample

1st rowFRM00615
2nd rowFRM01071
3rd rowFRM01410
4th rowFRM01603
5th rowGULEKS TEKSTIL
ValueCountFrequency (%)
mustafa 2
 
0.4%
ltd 2
 
0.4%
frm02935 1
 
0.2%
016 1
 
0.2%
015 1
 
0.2%
014 1
 
0.2%
013 1
 
0.2%
012 1
 
0.2%
010 1
 
0.2%
f0000136 1
 
0.2%
Other values (487) 487
97.6%
2025-03-19T17:50:42.984885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1403
39.6%
F 277
 
7.8%
1 189
 
5.3%
3 156
 
4.4%
M 153
 
4.3%
2 141
 
4.0%
O 123
 
3.5%
L 122
 
3.4%
R 117
 
3.3%
Y 115
 
3.2%
Other values (33) 748
21.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2347
66.2%
Uppercase Letter 1168
33.0%
Space Separator 16
 
0.5%
Lowercase Letter 12
 
0.3%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 277
23.7%
M 153
13.1%
O 123
10.5%
L 122
10.4%
R 117
10.0%
Y 115
9.8%
A 71
 
6.1%
E 33
 
2.8%
T 25
 
2.1%
I 22
 
1.9%
Other values (16) 110
 
9.4%
Decimal Number
ValueCountFrequency (%)
0 1403
59.8%
1 189
 
8.1%
3 156
 
6.6%
2 141
 
6.0%
4 89
 
3.8%
9 82
 
3.5%
8 77
 
3.3%
5 74
 
3.2%
6 69
 
2.9%
7 67
 
2.9%
Lowercase Letter
ValueCountFrequency (%)
a 4
33.3%
t 2
16.7%
s 2
16.7%
u 2
16.7%
f 2
16.7%
Space Separator
ValueCountFrequency (%)
16
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2364
66.7%
Latin 1180
33.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 277
23.5%
M 153
13.0%
O 123
10.4%
L 122
10.3%
R 117
9.9%
Y 115
9.7%
A 71
 
6.0%
E 33
 
2.8%
T 25
 
2.1%
I 22
 
1.9%
Other values (21) 122
10.3%
Common
ValueCountFrequency (%)
0 1403
59.3%
1 189
 
8.0%
3 156
 
6.6%
2 141
 
6.0%
4 89
 
3.8%
9 82
 
3.5%
8 77
 
3.3%
5 74
 
3.1%
6 69
 
2.9%
7 67
 
2.8%
Other values (2) 17
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3544
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1403
39.6%
F 277
 
7.8%
1 189
 
5.3%
3 156
 
4.4%
M 153
 
4.3%
2 141
 
4.0%
O 123
 
3.5%
L 122
 
3.4%
R 117
 
3.3%
Y 115
 
3.2%
Other values (33) 748
21.1%

Email
Text

Missing 

Distinct187
Distinct (%)94.4%
Missing285
Missing (%)59.0%
Memory size26.9 KiB
2025-03-19T17:50:43.143997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length43
Median length32.5
Mean length24.338384
Min length11

Characters and Unicode

Total characters4819
Distinct characters57
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique177 ?
Unique (%)89.4%

Sample

1st rowanabelduc@filmod.fr
2nd rowanas.ghali@syinternational.tn
3rd rowsunny@texvillbd.com
4th rowalessandro@francofrati.it
5th rowali@mailleconcept.com
ValueCountFrequency (%)
gestionlocative@nhood.com 3
 
1.5%
sunshine@nahuli.com 2
 
1.0%
etardy@altarea.com 2
 
1.0%
guillaume.bourdet@groupe-elancia.fr 2
 
1.0%
nsorand@galimmo.com 2
 
1.0%
sindy.klock@espace-expansion.fr 2
 
1.0%
gaelle.dendele@espace-expansion.fr 2
 
1.0%
jauffret@mercialys.com 2
 
1.0%
shaidy@mercialys.com 2
 
1.0%
gl_contact_client@klepierre.com 2
 
1.0%
Other values (177) 177
89.4%
2025-03-19T17:50:43.384194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 404
 
8.4%
a 396
 
8.2%
o 363
 
7.5%
i 340
 
7.1%
c 321
 
6.7%
r 310
 
6.4%
t 274
 
5.7%
. 269
 
5.6%
n 262
 
5.4%
m 260
 
5.4%
Other values (47) 1620
33.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4166
86.4%
Other Punctuation 465
 
9.6%
Uppercase Letter 87
 
1.8%
Decimal Number 51
 
1.1%
Dash Punctuation 42
 
0.9%
Connector Punctuation 8
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 404
9.7%
a 396
 
9.5%
o 363
 
8.7%
i 340
 
8.2%
c 321
 
7.7%
r 310
 
7.4%
t 274
 
6.6%
n 262
 
6.3%
m 260
 
6.2%
l 233
 
5.6%
Other values (16) 1003
24.1%
Uppercase Letter
ValueCountFrequency (%)
C 13
14.9%
T 11
12.6%
I 8
9.2%
A 7
8.0%
L 7
8.0%
O 7
8.0%
E 6
6.9%
N 6
6.9%
S 5
 
5.7%
G 4
 
4.6%
Other values (8) 13
14.9%
Decimal Number
ValueCountFrequency (%)
2 10
19.6%
0 10
19.6%
1 8
15.7%
6 7
13.7%
9 6
11.8%
7 3
 
5.9%
5 3
 
5.9%
3 2
 
3.9%
8 2
 
3.9%
Other Punctuation
ValueCountFrequency (%)
. 269
57.8%
@ 196
42.2%
Dash Punctuation
ValueCountFrequency (%)
- 42
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4253
88.3%
Common 566
 
11.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 404
 
9.5%
a 396
 
9.3%
o 363
 
8.5%
i 340
 
8.0%
c 321
 
7.5%
r 310
 
7.3%
t 274
 
6.4%
n 262
 
6.2%
m 260
 
6.1%
l 233
 
5.5%
Other values (34) 1090
25.6%
Common
ValueCountFrequency (%)
. 269
47.5%
@ 196
34.6%
- 42
 
7.4%
2 10
 
1.8%
0 10
 
1.8%
_ 8
 
1.4%
1 8
 
1.4%
6 7
 
1.2%
9 6
 
1.1%
7 3
 
0.5%
Other values (3) 7
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4819
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 404
 
8.4%
a 396
 
8.2%
o 363
 
7.5%
i 340
 
7.1%
c 321
 
6.7%
r 310
 
6.4%
t 274
 
5.7%
. 269
 
5.6%
n 262
 
5.4%
m 260
 
5.4%
Other values (47) 1620
33.6%

Tel
Text

Missing 

Distinct250
Distinct (%)92.6%
Missing213
Missing (%)44.1%
Memory size26.8 KiB
2025-03-19T17:50:43.549064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length20
Median length19
Mean length13.192593
Min length8

Characters and Unicode

Total characters3562
Distinct characters24
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique239 ?
Unique (%)88.5%

Sample

1st row212 5 22 71 76 76
2nd row01 84 88 03 48
3rd row01819116522
4th row01 40 37 80 31
5th row00 216 98749105
ValueCountFrequency (%)
33 33
 
3.9%
01 29
 
3.4%
03 26
 
3.1%
04 26
 
3.1%
00 23
 
2.7%
02 15
 
1.8%
06 14
 
1.6%
70 14
 
1.6%
55 13
 
1.5%
72 12
 
1.4%
Other values (261) 646
75.9%
2025-03-19T17:50:43.783439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
581
16.3%
0 553
15.5%
3 344
9.7%
4 283
7.9%
1 277
7.8%
7 263
7.4%
2 249
7.0%
6 230
 
6.5%
5 225
 
6.3%
8 211
 
5.9%
Other values (14) 346
9.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2809
78.9%
Space Separator 581
 
16.3%
Other Punctuation 69
 
1.9%
Math Symbol 45
 
1.3%
Open Punctuation 18
 
0.5%
Close Punctuation 18
 
0.5%
Dash Punctuation 14
 
0.4%
Lowercase Letter 8
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 553
19.7%
3 344
12.2%
4 283
10.1%
1 277
9.9%
7 263
9.4%
2 249
8.9%
6 230
8.2%
5 225
8.0%
8 211
 
7.5%
9 174
 
6.2%
Lowercase Letter
ValueCountFrequency (%)
m 2
25.0%
g 1
12.5%
a 1
12.5%
i 1
12.5%
l 1
12.5%
c 1
12.5%
o 1
12.5%
Other Punctuation
ValueCountFrequency (%)
. 67
97.1%
/ 2
 
2.9%
Space Separator
ValueCountFrequency (%)
581
100.0%
Math Symbol
ValueCountFrequency (%)
+ 45
100.0%
Open Punctuation
ValueCountFrequency (%)
( 18
100.0%
Close Punctuation
ValueCountFrequency (%)
) 18
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3554
99.8%
Latin 8
 
0.2%

Most frequent character per script

Common
ValueCountFrequency (%)
581
16.3%
0 553
15.6%
3 344
9.7%
4 283
8.0%
1 277
7.8%
7 263
7.4%
2 249
7.0%
6 230
 
6.5%
5 225
 
6.3%
8 211
 
5.9%
Other values (7) 338
9.5%
Latin
ValueCountFrequency (%)
m 2
25.0%
g 1
12.5%
a 1
12.5%
i 1
12.5%
l 1
12.5%
c 1
12.5%
o 1
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3562
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
581
16.3%
0 553
15.5%
3 344
9.7%
4 283
7.9%
1 277
7.8%
7 263
7.4%
2 249
7.0%
6 230
 
6.5%
5 225
 
6.3%
8 211
 
5.9%
Other values (14) 346
9.7%

Fax
Text

Missing 

Distinct62
Distinct (%)98.4%
Missing420
Missing (%)87.0%
Memory size20.7 KiB
2025-03-19T17:50:43.880170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length27
Median length21
Mean length13.253968
Min length9

Characters and Unicode

Total characters835
Distinct characters31
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique61 ?
Unique (%)96.8%

Sample

1st row212 5 22 71 73 54
2nd row507 33 42
3rd row0387514220
4th rowisabellevalenza@hotmail.com
5th row0148242797
ValueCountFrequency (%)
01 11
 
5.7%
04 10
 
5.2%
33 9
 
4.7%
03 7
 
3.6%
02 5
 
2.6%
72 4
 
2.1%
08 4
 
2.1%
67 4
 
2.1%
09 3
 
1.6%
05 3
 
1.6%
Other values (101) 133
68.9%
2025-03-19T17:50:44.083713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
130
15.6%
0 110
13.2%
2 70
8.4%
4 70
8.4%
3 70
8.4%
1 62
7.4%
7 62
7.4%
8 51
 
6.1%
9 49
 
5.9%
6 49
 
5.9%
Other values (21) 112
13.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 640
76.6%
Space Separator 130
 
15.6%
Lowercase Letter 25
 
3.0%
Other Punctuation 24
 
2.9%
Math Symbol 5
 
0.6%
Dash Punctuation 5
 
0.6%
Open Punctuation 3
 
0.4%
Close Punctuation 3
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 4
16.0%
l 4
16.0%
e 3
12.0%
m 2
8.0%
o 2
8.0%
i 2
8.0%
s 1
 
4.0%
b 1
 
4.0%
h 1
 
4.0%
z 1
 
4.0%
Other values (4) 4
16.0%
Decimal Number
ValueCountFrequency (%)
0 110
17.2%
2 70
10.9%
4 70
10.9%
3 70
10.9%
1 62
9.7%
7 62
9.7%
8 51
8.0%
9 49
7.7%
6 49
7.7%
5 47
7.3%
Other Punctuation
ValueCountFrequency (%)
. 23
95.8%
@ 1
 
4.2%
Space Separator
ValueCountFrequency (%)
130
100.0%
Math Symbol
ValueCountFrequency (%)
+ 5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 810
97.0%
Latin 25
 
3.0%

Most frequent character per script

Common
ValueCountFrequency (%)
130
16.0%
0 110
13.6%
2 70
8.6%
4 70
8.6%
3 70
8.6%
1 62
7.7%
7 62
7.7%
8 51
 
6.3%
9 49
 
6.0%
6 49
 
6.0%
Other values (7) 87
10.7%
Latin
ValueCountFrequency (%)
a 4
16.0%
l 4
16.0%
e 3
12.0%
m 2
8.0%
o 2
8.0%
i 2
8.0%
s 1
 
4.0%
b 1
 
4.0%
h 1
 
4.0%
z 1
 
4.0%
Other values (4) 4
16.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
130
15.6%
0 110
13.2%
2 70
8.4%
4 70
8.4%
3 70
8.4%
1 62
7.4%
7 62
7.4%
8 51
 
6.1%
9 49
 
5.9%
6 49
 
5.9%
Other values (21) 112
13.4%

Chiffre
Real number (ℝ)

High correlation  Zeros 

Distinct355
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51061.508
Minimum0
Maximum1954887.1
Zeros126
Zeros (%)26.1%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2025-03-19T17:50:44.161970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4016.354
Q330546.265
95-th percentile254007.32
Maximum1954887.1
Range1954887.1
Interquartile range (IQR)30546.265

Descriptive statistics

Standard deviation146626.68
Coefficient of variation (CV)2.8715697
Kurtosis71.803637
Mean51061.508
Median Absolute Deviation (MAD)4016.354
Skewness7.1298001
Sum24662708
Variance2.1499383 × 1010
MonotonicityNot monotonic
2025-03-19T17:50:44.271342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 126
 
26.1%
372 2
 
0.4%
3000 2
 
0.4%
850 2
 
0.4%
480 1
 
0.2%
1508.388 1
 
0.2%
2230.56 1
 
0.2%
54 1
 
0.2%
1020 1
 
0.2%
925 1
 
0.2%
Other values (345) 345
71.4%
ValueCountFrequency (%)
0 126
26.1%
8.08 1
 
0.2%
18 1
 
0.2%
36 1
 
0.2%
48 1
 
0.2%
54 1
 
0.2%
56.51 1
 
0.2%
60 1
 
0.2%
91.44 1
 
0.2%
92.4 1
 
0.2%
ValueCountFrequency (%)
1954887.06 1
0.2%
1207569.416 1
0.2%
872721.06 1
0.2%
839037.014 1
0.2%
790056.016 1
0.2%
678859.682 1
0.2%
589731.67 1
0.2%
455731.964 1
0.2%
432111.4 1
0.2%
419750.18 1
0.2%

Reglements
Real number (ℝ)

High correlation  Zeros 

Distinct293
Distinct (%)60.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31883.573
Minimum0
Maximum1420220.7
Zeros189
Zeros (%)39.1%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2025-03-19T17:50:44.356404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median780
Q320909.011
95-th percentile147442.18
Maximum1420220.7
Range1420220.7
Interquartile range (IQR)20909.011

Descriptive statistics

Standard deviation96642.581
Coefficient of variation (CV)3.0311088
Kurtosis95.612032
Mean31883.573
Median Absolute Deviation (MAD)780
Skewness8.0880211
Sum15399766
Variance9.3397884 × 109
MonotonicityNot monotonic
2025-03-19T17:50:44.452614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 189
39.1%
372 2
 
0.4%
850 2
 
0.4%
6509.5 1
 
0.2%
12978.36 1
 
0.2%
18600 1
 
0.2%
2958 1
 
0.2%
9445.745 1
 
0.2%
115.2 1
 
0.2%
60 1
 
0.2%
Other values (283) 283
58.6%
ValueCountFrequency (%)
0 189
39.1%
8.08 1
 
0.2%
18 1
 
0.2%
36 1
 
0.2%
54 1
 
0.2%
60 1
 
0.2%
91.44 1
 
0.2%
92.4 1
 
0.2%
104.94 1
 
0.2%
106.32 1
 
0.2%
ValueCountFrequency (%)
1420220.68 1
0.2%
639357.77 1
0.2%
573078.5 1
0.2%
468342.732 1
0.2%
437510.4 1
0.2%
415477.68 1
0.2%
407082.763 1
0.2%
402241.67 1
0.2%
399852.864 1
0.2%
280392.5 1
0.2%

Solde
Real number (ℝ)

High correlation  Zeros 

Distinct224
Distinct (%)46.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19177.93
Minimum-17129.6
Maximum634490.92
Zeros260
Zeros (%)53.8%
Negative9
Negative (%)1.9%
Memory size7.5 KiB
2025-03-19T17:50:44.553259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-17129.6
5-th percentile0
Q10
median0
Q311742.655
95-th percentile98654.041
Maximum634490.92
Range651620.52
Interquartile range (IQR)11742.655

Descriptive statistics

Standard deviation59693.039
Coefficient of variation (CV)3.1125903
Kurtosis45.416294
Mean19177.93
Median Absolute Deviation (MAD)0
Skewness6.0215562
Sum9262940.3
Variance3.5632589 × 109
MonotonicityNot monotonic
2025-03-19T17:50:44.649278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 260
53.8%
534666.38 1
 
0.2%
634490.916 1
 
0.2%
269901.12 1
 
0.2%
8594.76 1
 
0.2%
3539.376 1
 
0.2%
32645.16 1
 
0.2%
5489.232 1
 
0.2%
187490 1
 
0.2%
497.12 1
 
0.2%
Other values (214) 214
44.3%
ValueCountFrequency (%)
-17129.6 1
 
0.2%
-7505.4 1
 
0.2%
-3670.452 1
 
0.2%
-678.624 1
 
0.2%
-1 1
 
0.2%
-0.4 1
 
0.2%
-0.008 1
 
0.2%
-0.005 1
 
0.2%
-0.001 1
 
0.2%
0 260
53.8%
ValueCountFrequency (%)
634490.916 1
0.2%
534666.38 1
0.2%
435210.66 1
0.2%
382973.219 1
0.2%
370694.26 1
0.2%
282443.036 1
0.2%
269901.12 1
0.2%
213770.3 1
0.2%
192598.986 1
0.2%
190746.216 1
0.2%

MF
Text

Missing 

Distinct211
Distinct (%)100.0%
Missing272
Missing (%)56.3%
Memory size25.4 KiB
2025-03-19T17:50:44.822797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length18
Median length14
Mean length14.672986
Min length7

Characters and Unicode

Total characters3096
Distinct characters19
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique211 ?
Unique (%)100.0%

Sample

1st row82808622300017
2nd row52783327100044
3rd row345663/293245
4th row512152588 00046
5th row48437889800031
ValueCountFrequency (%)
000 5
 
1.3%
00010 4
 
1.0%
00013 4
 
1.0%
783 4
 
1.0%
775 4
 
1.0%
00011 3
 
0.8%
788 3
 
0.8%
289 3
 
0.8%
778 3
 
0.8%
195 2
 
0.5%
Other values (329) 352
91.0%
2025-03-19T17:50:45.050943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 789
25.5%
1 282
 
9.1%
3 275
 
8.9%
4 253
 
8.2%
2 245
 
7.9%
8 241
 
7.8%
7 235
 
7.6%
5 213
 
6.9%
9 200
 
6.5%
176
 
5.7%
Other values (9) 187
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2902
93.7%
Space Separator 176
 
5.7%
Other Punctuation 11
 
0.4%
Uppercase Letter 6
 
0.2%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 789
27.2%
1 282
 
9.7%
3 275
 
9.5%
4 253
 
8.7%
2 245
 
8.4%
8 241
 
8.3%
7 235
 
8.1%
5 213
 
7.3%
9 200
 
6.9%
6 169
 
5.8%
Uppercase Letter
ValueCountFrequency (%)
M 2
33.3%
R 1
16.7%
P 1
16.7%
L 1
16.7%
A 1
16.7%
Other Punctuation
ValueCountFrequency (%)
/ 8
72.7%
. 3
 
27.3%
Space Separator
ValueCountFrequency (%)
176
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3090
99.8%
Latin 6
 
0.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 789
25.5%
1 282
 
9.1%
3 275
 
8.9%
4 253
 
8.2%
2 245
 
7.9%
8 241
 
7.8%
7 235
 
7.6%
5 213
 
6.9%
9 200
 
6.5%
176
 
5.7%
Other values (4) 181
 
5.9%
Latin
ValueCountFrequency (%)
M 2
33.3%
R 1
16.7%
P 1
16.7%
L 1
16.7%
A 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3096
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 789
25.5%
1 282
 
9.1%
3 275
 
8.9%
4 253
 
8.2%
2 245
 
7.9%
8 241
 
7.8%
7 235
 
7.6%
5 213
 
6.9%
9 200
 
6.5%
176
 
5.7%
Other values (9) 187
 
6.0%

Observations
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size29.2 KiB
None
481 
DOLAR IBAN: TR65 0020 9000 0056 6062 0000 13 EUR IBAN : TR49 0020 9000 0056 6062 0000 10 SWIFT KODU : ZKBATRIS ZIRAAT KATILIM BANKASI / BRANCH: NILÜFER
 
1
AGENT : LOTE KNITWEAR SWIFT CODE : TCZBTR2A RIB : TR48 0001 0026 9267 7865 3950 03
 
1

Length

Max length155
Median length4
Mean length4.4782609
Min length4

Characters and Unicode

Total characters2163
Distinct characters39
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.4%

Sample

1st rowNone
2nd rowNone
3rd rowNone
4th rowNone
5th rowNone

Common Values

ValueCountFrequency (%)
None 481
99.6%
DOLAR IBAN: TR65 0020 9000 0056 6062 0000 13 EUR IBAN : TR49 0020 9000 0056 6062 0000 10 SWIFT KODU : ZKBATRIS ZIRAAT KATILIM BANKASI / BRANCH: NILÜFER 1
 
0.2%
AGENT : LOTE KNITWEAR SWIFT CODE : TCZBTR2A RIB : TR48 0001 0026 9267 7865 3950 03 1
 
0.2%

Length

2025-03-19T17:50:45.130611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T17:50:45.193971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
none 481
91.3%
6
 
1.1%
iban 2
 
0.4%
0000 2
 
0.4%
0020 2
 
0.4%
0056 2
 
0.4%
9000 2
 
0.4%
swift 2
 
0.4%
6062 2
 
0.4%
dolar 1
 
0.2%
Other values (25) 25
 
4.7%

Most occurring characters

ValueCountFrequency (%)
N 488
22.6%
o 481
22.2%
n 481
22.2%
e 481
22.2%
40
 
1.8%
0 34
 
1.6%
T 13
 
0.6%
A 13
 
0.6%
I 12
 
0.6%
R 12
 
0.6%
Other values (29) 108
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1443
66.7%
Uppercase Letter 589
27.2%
Decimal Number 73
 
3.4%
Space Separator 40
 
1.8%
Control 10
 
0.5%
Other Punctuation 8
 
0.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 488
82.9%
T 13
 
2.2%
A 13
 
2.2%
I 12
 
2.0%
R 12
 
2.0%
B 7
 
1.2%
E 6
 
1.0%
K 5
 
0.8%
S 4
 
0.7%
O 4
 
0.7%
Other values (11) 25
 
4.2%
Decimal Number
ValueCountFrequency (%)
0 34
46.6%
6 10
 
13.7%
2 7
 
9.6%
5 5
 
6.8%
9 5
 
6.8%
3 3
 
4.1%
1 3
 
4.1%
4 2
 
2.7%
7 2
 
2.7%
8 2
 
2.7%
Lowercase Letter
ValueCountFrequency (%)
o 481
33.3%
n 481
33.3%
e 481
33.3%
Other Punctuation
ValueCountFrequency (%)
: 7
87.5%
/ 1
 
12.5%
Control
ValueCountFrequency (%)
5
50.0%
5
50.0%
Space Separator
ValueCountFrequency (%)
40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2032
93.9%
Common 131
 
6.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 488
24.0%
o 481
23.7%
n 481
23.7%
e 481
23.7%
T 13
 
0.6%
A 13
 
0.6%
I 12
 
0.6%
R 12
 
0.6%
B 7
 
0.3%
E 6
 
0.3%
Other values (14) 38
 
1.9%
Common
ValueCountFrequency (%)
40
30.5%
0 34
26.0%
6 10
 
7.6%
2 7
 
5.3%
: 7
 
5.3%
5
 
3.8%
5
 
3.8%
5 5
 
3.8%
9 5
 
3.8%
3 3
 
2.3%
Other values (5) 10
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2162
> 99.9%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 488
22.6%
o 481
22.2%
n 481
22.2%
e 481
22.2%
40
 
1.9%
0 34
 
1.6%
T 13
 
0.6%
A 13
 
0.6%
I 12
 
0.6%
R 12
 
0.6%
Other values (28) 107
 
4.9%
None
ValueCountFrequency (%)
Ü 1
100.0%

IDDevise
Categorical

High correlation 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size27.4 KiB
1
397 
2
62 
0
 
24

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters483
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 397
82.2%
2 62
 
12.8%
0 24
 
5.0%

Length

2025-03-19T17:50:45.464286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T17:50:45.528392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 397
82.2%
2 62
 
12.8%
0 24
 
5.0%

Most occurring characters

ValueCountFrequency (%)
1 397
82.2%
2 62
 
12.8%
0 24
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 483
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 397
82.2%
2 62
 
12.8%
0 24
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
Common 483
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 397
82.2%
2 62
 
12.8%
0 24
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 483
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 397
82.2%
2 62
 
12.8%
0 24
 
5.0%

IDCategorie
Real number (ℝ)

High correlation  Zeros 

Distinct56
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.233954
Minimum0
Maximum74
Zeros72
Zeros (%)14.9%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2025-03-19T17:50:45.599624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median11
Q318
95-th percentile50.9
Maximum74
Range74
Interquartile range (IQR)17

Descriptive statistics

Standard deviation15.689053
Coefficient of variation (CV)1.1855151
Kurtosis2.6548895
Mean13.233954
Median Absolute Deviation (MAD)10
Skewness1.6690641
Sum6392
Variance246.1464
MonotonicityNot monotonic
2025-03-19T17:50:45.697309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 123
25.5%
18 110
22.8%
0 72
14.9%
11 38
 
7.9%
16 13
 
2.7%
7 10
 
2.1%
22 9
 
1.9%
2 6
 
1.2%
14 6
 
1.2%
9 5
 
1.0%
Other values (46) 91
18.8%
ValueCountFrequency (%)
0 72
14.9%
1 123
25.5%
2 6
 
1.2%
4 4
 
0.8%
5 1
 
0.2%
6 4
 
0.8%
7 10
 
2.1%
8 3
 
0.6%
9 5
 
1.0%
10 4
 
0.8%
ValueCountFrequency (%)
74 1
 
0.2%
73 1
 
0.2%
71 1
 
0.2%
69 1
 
0.2%
68 1
 
0.2%
64 2
0.4%
63 1
 
0.2%
61 3
0.6%
60 1
 
0.2%
59 1
 
0.2%

FournitPF
Categorical

High correlation 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size27.4 KiB
0
400 
1
83 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters483
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 400
82.8%
1 83
 
17.2%

Length

2025-03-19T17:50:45.775580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T17:50:45.822456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 400
82.8%
1 83
 
17.2%

Most occurring characters

ValueCountFrequency (%)
0 400
82.8%
1 83
 
17.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 483
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 400
82.8%
1 83
 
17.2%

Most occurring scripts

ValueCountFrequency (%)
Common 483
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 400
82.8%
1 83
 
17.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 483
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 400
82.8%
1 83
 
17.2%

IDPays
Real number (ℝ)

High correlation  Zeros 

Distinct19
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean141.18427
Minimum0
Maximum213
Zeros131
Zeros (%)27.1%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2025-03-19T17:50:45.871648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median201
Q3201
95-th percentile201
Maximum213
Range213
Interquartile range (IQR)201

Descriptive statistics

Standard deviation89.233953
Coefficient of variation (CV)0.63203894
Kurtosis-1.124662
Mean141.18427
Median Absolute Deviation (MAD)0
Skewness-0.90900464
Sum68192
Variance7962.6983
MonotonicityNot monotonic
2025-03-19T17:50:45.937233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
201 297
61.5%
0 131
27.1%
171 21
 
4.3%
103 7
 
1.4%
204 6
 
1.2%
172 5
 
1.0%
18 4
 
0.8%
165 1
 
0.2%
99 1
 
0.2%
203 1
 
0.2%
Other values (9) 9
 
1.9%
ValueCountFrequency (%)
0 131
27.1%
18 4
 
0.8%
35 1
 
0.2%
99 1
 
0.2%
103 7
 
1.4%
107 1
 
0.2%
165 1
 
0.2%
171 21
 
4.3%
172 5
 
1.0%
173 1
 
0.2%
ValueCountFrequency (%)
213 1
 
0.2%
212 1
 
0.2%
211 1
 
0.2%
207 1
 
0.2%
204 6
 
1.2%
203 1
 
0.2%
202 1
 
0.2%
201 297
61.5%
200 1
 
0.2%
173 1
 
0.2%

Pays
Categorical

High correlation  Missing 

Distinct12
Distinct (%)18.8%
Missing419
Missing (%)86.7%
Memory size29.9 KiB
CN
18 
IN
14 
TR
13 
FR
6 
MA
3 
Other values (7)
10 

Length

Max length7
Median length2
Mean length2.078125
Min length2

Characters and Unicode

Total characters133
Distinct characters20
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)7.8%

Sample

1st rowTR
2nd rowIN
3rd rowCN
4th rowCN
5th rowTR

Common Values

ValueCountFrequency (%)
CN 18
 
3.7%
IN 14
 
2.9%
TR 13
 
2.7%
FR 6
 
1.2%
MA 3
 
0.6%
ES 3
 
0.6%
BD 2
 
0.4%
IT 1
 
0.2%
GB 1
 
0.2%
KR 1
 
0.2%
Other values (2) 2
 
0.4%
(Missing) 419
86.7%

Length

2025-03-19T17:50:46.006212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cn 18
28.1%
in 14
21.9%
tr 13
20.3%
fr 6
 
9.4%
ma 3
 
4.7%
es 3
 
4.7%
bd 2
 
3.1%
it 1
 
1.6%
gb 1
 
1.6%
kr 1
 
1.6%
Other values (2) 2
 
3.1%

Most occurring characters

ValueCountFrequency (%)
N 32
24.1%
R 20
15.0%
C 18
13.5%
I 15
11.3%
T 15
11.3%
F 6
 
4.5%
M 3
 
2.3%
A 3
 
2.3%
E 3
 
2.3%
S 3
 
2.3%
Other values (10) 15
11.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 127
95.5%
Lowercase Letter 6
 
4.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 32
25.2%
R 20
15.7%
C 18
14.2%
I 15
11.8%
T 15
11.8%
F 6
 
4.7%
M 3
 
2.4%
A 3
 
2.4%
E 3
 
2.4%
S 3
 
2.4%
Other values (5) 9
 
7.1%
Lowercase Letter
ValueCountFrequency (%)
i 2
33.3%
u 1
16.7%
n 1
16.7%
s 1
16.7%
e 1
16.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 133
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 32
24.1%
R 20
15.0%
C 18
13.5%
I 15
11.3%
T 15
11.3%
F 6
 
4.5%
M 3
 
2.3%
A 3
 
2.3%
E 3
 
2.3%
S 3
 
2.3%
Other values (10) 15
11.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 133
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 32
24.1%
R 20
15.0%
C 18
13.5%
I 15
11.3%
T 15
11.3%
F 6
 
4.5%
M 3
 
2.3%
A 3
 
2.3%
E 3
 
2.3%
S 3
 
2.3%
Other values (10) 15
11.3%

IDModeReglement
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size27.4 KiB
0
412 
1
58 
2
 
8
6
 
3
3
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters483
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 412
85.3%
1 58
 
12.0%
2 8
 
1.7%
6 3
 
0.6%
3 2
 
0.4%

Length

2025-03-19T17:50:46.088892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T17:50:46.133470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 412
85.3%
1 58
 
12.0%
2 8
 
1.7%
6 3
 
0.6%
3 2
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 412
85.3%
1 58
 
12.0%
2 8
 
1.7%
6 3
 
0.6%
3 2
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 483
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 412
85.3%
1 58
 
12.0%
2 8
 
1.7%
6 3
 
0.6%
3 2
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 483
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 412
85.3%
1 58
 
12.0%
2 8
 
1.7%
6 3
 
0.6%
3 2
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 483
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 412
85.3%
1 58
 
12.0%
2 8
 
1.7%
6 3
 
0.6%
3 2
 
0.4%

Etat
Categorical

High correlation 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size27.4 KiB
1
405 
0
78 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters483
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 405
83.9%
0 78
 
16.1%

Length

2025-03-19T17:50:46.198566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T17:50:46.249693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 405
83.9%
0 78
 
16.1%

Most occurring characters

ValueCountFrequency (%)
1 405
83.9%
0 78
 
16.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 483
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 405
83.9%
0 78
 
16.1%

Most occurring scripts

ValueCountFrequency (%)
Common 483
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 405
83.9%
0 78
 
16.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 483
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 405
83.9%
0 78
 
16.1%

IDCGAFournisseur
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size27.4 KiB
0
461 
2
 
18
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters483
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 461
95.4%
2 18
 
3.7%
1 4
 
0.8%

Length

2025-03-19T17:50:46.302600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T17:50:46.351609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 461
95.4%
2 18
 
3.7%
1 4
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 461
95.4%
2 18
 
3.7%
1 4
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 483
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 461
95.4%
2 18
 
3.7%
1 4
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 483
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 461
95.4%
2 18
 
3.7%
1 4
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 483
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 461
95.4%
2 18
 
3.7%
1 4
 
0.8%

IsMP
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size27.4 KiB
0
482 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters483
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 482
99.8%
1 1
 
0.2%

Length

2025-03-19T17:50:46.416596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T17:50:46.449263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 482
99.8%
1 1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 482
99.8%
1 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 483
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 482
99.8%
1 1
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 483
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 482
99.8%
1 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 483
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 482
99.8%
1 1
 
0.2%

IsPF
Categorical

High correlation 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size27.4 KiB
0
254 
1
229 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters483
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 254
52.6%
1 229
47.4%

Length

2025-03-19T17:50:46.516363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T17:50:46.548891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 254
52.6%
1 229
47.4%

Most occurring characters

ValueCountFrequency (%)
0 254
52.6%
1 229
47.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 483
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 254
52.6%
1 229
47.4%

Most occurring scripts

ValueCountFrequency (%)
Common 483
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 254
52.6%
1 229
47.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 483
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 254
52.6%
1 229
47.4%

isService
Categorical

High correlation 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size27.4 KiB
1
318 
0
165 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters483
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 318
65.8%
0 165
34.2%

Length

2025-03-19T17:50:46.617268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T17:50:46.665293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 318
65.8%
0 165
34.2%

Most occurring characters

ValueCountFrequency (%)
1 318
65.8%
0 165
34.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 483
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 318
65.8%
0 165
34.2%

Most occurring scripts

ValueCountFrequency (%)
Common 483
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 318
65.8%
0 165
34.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 483
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 318
65.8%
0 165
34.2%

CodeTVA
Text

Missing 

Distinct245
Distinct (%)96.1%
Missing228
Missing (%)47.2%
Memory size26.5 KiB
2025-03-19T17:50:46.776009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length20
Median length13
Mean length13.631373
Min length11

Characters and Unicode

Total characters3476
Distinct characters19
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique240 ?
Unique (%)94.1%

Sample

1st rowFR23828086223
2nd rowFR 45 527833271
3rd rowV.D. 621 048 2909
4th rowFR 86512152588
5th rowFR76484378898
ValueCountFrequency (%)
fr 52
 
12.7%
fr42424064707 5
 
1.2%
778 4
 
1.0%
fr79410034607 3
 
0.7%
fr17784364150 3
 
0.7%
796 2
 
0.5%
fr02 2
 
0.5%
000 2
 
0.5%
23 2
 
0.5%
950 2
 
0.5%
Other values (326) 331
81.1%
2025-03-19T17:50:46.981625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 318
9.1%
7 305
8.8%
2 302
8.7%
8 300
8.6%
3 299
8.6%
0 288
8.3%
1 267
7.7%
5 259
7.5%
F 251
 
7.2%
R 251
 
7.2%
Other values (9) 636
18.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2812
80.9%
Uppercase Letter 508
 
14.6%
Space Separator 153
 
4.4%
Other Punctuation 2
 
0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 318
11.3%
7 305
10.8%
2 302
10.7%
8 300
10.7%
3 299
10.6%
0 288
10.2%
1 267
9.5%
5 259
9.2%
9 248
8.8%
6 226
8.0%
Uppercase Letter
ValueCountFrequency (%)
F 251
49.4%
R 251
49.4%
B 2
 
0.4%
G 2
 
0.4%
V 1
 
0.2%
D 1
 
0.2%
Space Separator
ValueCountFrequency (%)
153
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2968
85.4%
Latin 508
 
14.6%

Most frequent character per script

Common
ValueCountFrequency (%)
4 318
10.7%
7 305
10.3%
2 302
10.2%
8 300
10.1%
3 299
10.1%
0 288
9.7%
1 267
9.0%
5 259
8.7%
9 248
8.4%
6 226
7.6%
Other values (3) 156
5.3%
Latin
ValueCountFrequency (%)
F 251
49.4%
R 251
49.4%
B 2
 
0.4%
G 2
 
0.4%
V 1
 
0.2%
D 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3476
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 318
9.1%
7 305
8.8%
2 302
8.7%
8 300
8.6%
3 299
8.6%
0 288
8.3%
1 267
7.7%
5 259
7.5%
F 251
 
7.2%
R 251
 
7.2%
Other values (9) 636
18.3%

Interactions

2025-03-19T17:50:40.352212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T17:50:37.310559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T17:50:37.848777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T17:50:38.478486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T17:50:39.041855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T17:50:39.587322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T17:50:40.455429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T17:50:37.399010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T17:50:37.993833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T17:50:38.582026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T17:50:39.122490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T17:50:39.684232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T17:50:40.554197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T17:50:37.491028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T17:50:38.093066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T17:50:38.675877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T17:50:39.212987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T17:50:39.768298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T17:50:40.646389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T17:50:37.585268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T17:50:38.186683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T17:50:38.772254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T17:50:39.316944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T17:50:39.872025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T17:50:40.738546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T17:50:37.668812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T17:50:38.275949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T17:50:38.853940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T17:50:39.407503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T17:50:39.961399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T17:50:40.821804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T17:50:37.759486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T17:50:38.386063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T17:50:38.956998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T17:50:39.513217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T17:50:40.272304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-03-19T17:50:47.064792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ChiffreEtatFournitPFIDCGAFournisseurIDCategorieIDDeviseIDFournisseurIDModeReglementIDPaysIsMPIsPFObservationsPaysReglementsSoldeisService
Chiffre1.0000.0590.1520.3800.3500.0000.2370.0000.4860.0000.1640.0000.2560.8180.7350.175
Etat0.0591.0000.9110.0000.4600.7220.8960.7710.6520.0000.4320.0000.3860.0320.0680.602
FournitPF0.1520.9111.0000.1930.4790.7100.9620.8400.6250.0000.4280.0001.0000.1150.1560.591
IDCGAFournisseur0.3800.0000.1931.0000.0990.0360.3130.3410.5600.0000.1140.0000.4470.3740.3760.188
IDCategorie0.3500.4600.4790.0991.0000.3340.1350.1820.7280.0000.3300.0001.0000.6080.0650.768
IDDevise0.0000.7220.7100.0360.3341.0000.6290.4810.4940.0000.4280.0000.6450.0000.0000.632
IDFournisseur0.2370.8960.9620.3130.1350.6291.0000.5010.2080.1450.8310.0000.1330.0360.1960.721
IDModeReglement0.0000.7710.8400.3410.1820.4810.5011.0000.4580.0000.3930.0000.6430.0000.0000.533
IDPays0.4860.6520.6250.5600.7280.4940.2080.4581.0000.0000.3130.1510.4250.7360.1550.956
IsMP0.0000.0000.0000.0000.0000.0000.1450.0000.0001.0000.0000.0001.0000.0000.0000.000
IsPF0.1640.4320.4280.1140.3300.4280.8310.3930.3130.0001.0000.0220.3580.1180.2020.296
Observations0.0000.0000.0000.0000.0000.0000.0000.0000.1510.0000.0221.0001.0000.0000.0000.062
Pays0.2560.3861.0000.4471.0000.6450.1330.6430.4251.0000.3581.0001.0000.2560.2560.358
Reglements0.8180.0320.1150.3740.6080.0000.0360.0000.7360.0000.1180.0000.2561.0000.4120.143
Solde0.7350.0680.1560.3760.0650.0000.1960.0000.1550.0000.2020.0000.2560.4121.0000.202
isService0.1750.6020.5910.1880.7680.6320.7210.5330.9560.0000.2960.0620.3580.1430.2021.000

Missing values

2025-03-19T17:50:40.953950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-19T17:50:41.105927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-03-19T17:50:41.271314image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

IDFournisseurFournisseurAdresseCodeEmailTelFaxChiffreReglementsSoldeMFObservationsIDDeviseIDCategorieFournitPFIDPaysPaysIDModeReglementEtatIDCGAFournisseurIsMPIsPFisServiceCodeTVA
06BALINLER PAZARLAMA VE TICARETGULTEPE MAH TURE SOK N°12FRM00615NaNNaNNaN0.00.00.0NaNNone2110TR100010NaN
119DRISHTI APPARELSPLOT NO 180 SECTOR -6 IM MANESFRM01071NaNNaNNaN0.00.00.0NaNNone2110IN100010NaN
225TRUMODE INTERNATIONAL LTD.3-5F N0 9. LOFT POWER N0 28 XIFRM01410NaNNaNNaN0.00.00.0NaNNone2110CN100010NaN
329SHANGHAI SILK GROUP TRADING DEVELOPMENT CO., LTDN°283 WUXING ROADFRM01603NaNNaNNaN0.00.00.0NaNNone2110CN100010NaN
432GULEKS TEKSTIL SAN TIC LTD STIAMBARLI PETROL OFSI DOLUMGULEKS TEKSTILNaNNaNNaN0.00.00.0NaNNone111171TR111010NaN
533AIT APPAREL CO LTD3RD FLOOR BLOCK 8FRM01824NaNNaNNaN0.00.00.0NaNNone2110CN100010NaN
638ANTIK DIS TICARET LTD STIBEYSAN SANAYRI SITESI DEREBOYUFRM02214NaNNaNNaN0.00.00.0NaNNone2110TR100010NaN
739DEMKA TEKSTIL SAN DIS TIC ASKARAYOLLARI MAH 564-1 SOK N°1FRM02232NaNNaNNaN0.00.00.0NaNNone1110TR100010NaN
842DYNAMIC DESIGN INC.PLOT NO 417 PACE CITY IIFRM02303NaNNaNNaN0.00.00.0NaNNone2110IN100010NaN
946TIANJIN PUNLEET TRADING CO LTDRM 902 A03 NO 17 CENTURY BLDGFRM02435NaNNaNNaN0.00.00.0NaNNone2110CN100010NaN
IDFournisseurFournisseurAdresseCodeEmailTelFaxChiffreReglementsSoldeMFObservationsIDDeviseIDCategorieFournitPFIDPaysPaysIDModeReglementEtatIDCGAFournisseurIsMPIsPFisServiceCodeTVA
474704NOVAGRAAF2 Rue Sarah Bernhardt\r\nCS 90017\r\n92665 ASNIERES-SUR-SEINEF0000135tm.fr@novagraaf.com+33(0)1 49 64 60 00NaN1562.41562.40.063200155800071None1740201NaN010001FR 06 632 001 558
475705NONE MANAGEMENT GROUP12 ALLEE DES EGLANTINES 92260 FONTENAY-AUX-ROSESF0000136elie.zinsou@gmail.com90818641400011NaN41400.018600.022800.090818641400011None1140201NaN010001FR71908186414
476706ONATEKSNECIP FAZIL KISAKUREK MH. GAZI CD. NO:78 ESENYURT ISTANBUL010yasin.basoglu@onat.com.tr+902126894242+9021268908160.00.00.0NaNNone110171NaN312010NaN
477707UTG ISTANBUL GIYIM SAN. A.S.NAMIK KEMAL MAH. 120. SOK. NO:16/1 K:3 ESENYURT ISTANBUL012muhsin.arkon@utgistanbul.com.tr+90 5334797847NaN0.00.00.0NaNNone110171NaN310010NaN
478708NAHULI GUANGZHOU FASHION COMPANY LTDB1409,14TH FLOOR,CHINA PLAZA CHINA INT CENTRE BUILDING,NO.33 ZHONGSHAN 3 ROAD,GUANGZHOU013sunshine@nahuli.com86 13809774718NaN0.00.00.0NaNNone110103NaN010010NaN
479709SHINY STARS (HONGKONG) TRADING COMPANY LIMITEDUNIT A7 12/F ASTORIA BUILDING 34 ASHLEY ROAD TSIM SHA TSUI KL014sunshine@nahuli.com86 13809774718NaN0.00.00.0NaNNone110103NaN010010NaN
480710Zhuo Silun Wool Weaving FactoryNo. 5 501, Lin Cuo Industrial Zone, Jinlin Road, Lin Cuo Village, Outer Sand Street, Longhu district City\r\nShantou City015tim@classysweaterfactory.com86-188 2605 2175NaN0.00.00.0NaNNone210103NaN010010NaN
481711SAFI GROUP MAKINA GIDA SANAYI TICARET ITHALAT IHRA15 Temmuz Mah. 1506 sok. Bina No:7 No:31 BAGCILAR/ISTANBUL016ismail@safitekstil.com.tr0 551 408 54 08NaN0.00.00.0NaNNone110171NaN010010NaN
482712ZHEJIANG TRIMAX INTERNATIONAL GROUPBUILDING A AREA NO.900 HUZHI ROAD,WUXING DISTRICT, HUZHOU CITY, ZHEJIANG PROVINCE,CHINA017LOIS@TRIMAX.CC0086-572-2626087NaN0.00.00.0NaNNone210103NaN010010NaN
483713HK NEW WORLD FASHION IM & EX LIMITEDA-612, Changsheng Cloth Building, No.363 Shicha Road, Baiyun District, 510430, Guangzhou CHINA018cihan@hknewworldfashiongroup.com+86 - 15914378934NaN0.00.00.0NaNNone200103NaN010010NaN